Update the usage instructions.

README.md

@@ -560,12 +560,12 @@ configs:
 
 RMIS is a benchmark dataset collection for evaluating representation learning on **multi-modal industrial signals**. It brings together the datasets used in the RMIS benchmark, covering **anomaly detection** and **fault diagnosis** across four modalities: **sound, vibration, voltage, and current**.
 
-This Hugging Face repository is meant to provide the **benchmark datasets themselves** in an easy-to-load format. If you are looking for the full benchmark codebase, evaluation pipeline, or leaderboard, please refer to the [RMIS GitHub repository](https://github.com/jianganbai/RMIS).
+This Hugging Face repository mainly hosts the **benchmark datasets themselves**. If you are looking for the full benchmark codebase, evaluation pipeline, preprocessing details, or leaderboard, please refer to the [RMIS GitHub repository](https://github.com/jianganbai/RMIS).
 
 RMIS is closely related to **FISHER**:
 
 - **FISHER** is the foundation model proposed for industrial signal representation.
-- **RMIS** is the benchmark used to evaluate FISHER and other signal models.
+- **RMIS** is the benchmark used to evaluate FISHER and other signal foundation models.
 - This Hugging Face repository hosts the **dataset side** of RMIS, while the GitHub repository hosts the **benchmark code and evaluation pipeline**.
 
 In the current release, the dataset includes **19 configurations**:
@@ -582,73 +582,52 @@ The split names follow the benchmark tasks:
 - `ad`: anomaly detection subsets
 - `fd`: fault diagnosis subsets
 
-Please note that this repository focuses on **dataset hosting and loading**. Detailed benchmark construction, preprocessing rationale, evaluation protocol, and model integration are documented in the RMIS GitHub repository.
+Please note that this repository mainly serves as a **data hosting and distribution** endpoint. For RMIS-specific preprocessing, path organization, evaluation protocol, and model integration, please refer to the RMIS GitHub repository.
 
-## Usage
-
-Load one subset with `datasets`:
-
-```python
-from datasets import load_dataset
-
-ds = load_dataset("jiangab/RMIS", "dcase20", split="ad")
-print(ds)
-print(ds[0])
-```
-
-If you want decoded waveforms instead of deferred audio objects:
-
-```python
-from datasets import load_dataset, Audio
+## Recommended usage in the RMIS project
 
-ds = load_dataset("jiangab/RMIS", "dcase20", split="ad")
-ds = ds.cast_column("audio", Audio(sampling_rate=16000))
+If you want to use these datasets inside the **RMIS benchmark workflow**, please first download the [RMIS GitHub repository](https://github.com/jianganbai/RMIS), install its dependencies, and then run the provided script from that repository.
 
-sample = ds[0]
-audio = sample["audio"]["array"]
-sr = sample["audio"]["sampling_rate"]
-print(audio.shape, sr)
+```shell
+git clone https://github.com/jianganbai/RMIS.git
+cd RMIS/
+pip install -r requirements.txt
 ```
 
-Load another configuration in the same way:
-
-```python
-from datasets import load_dataset
+Then use the following command to download and extract the Hugging Face data into RMIS-compatible local wav folders:
 
-# fault diagnosis example
-fds = load_dataset("jiangab/RMIS", "umged_vib", split="fd")
-print(fds.column_names)
+```shell
+[HF_ENDPOINT=https://hf-mirror.com] python -m utils.scripts.download_and_extract_hf_data \
+    --output_dir OUTPUT_DIR \
+    [--subset SUBSET [SUBSET ...]] \
+    [--remove_parquet_after_extract] \
+    [--force_reextract]
 ```
 
-If you want to download the **entire dataset repository at once** instead of loading one configuration at a time, you can download the whole Hugging Face dataset repo locally:
-
-```python
-from huggingface_hub import snapshot_download
+For example:
 
-local_dir = snapshot_download(
-    repo_id="jiangab/RMIS",
-    repo_type="dataset",
-)
-print(local_dir)
+```shell
+python -m utils.scripts.download_and_extract_hf_data \
+    --output_dir datasets_hf \
+    --subset iiee mafaulda_sound \
+    --remove_parquet_after_extract
 ```
 
-You can also use the Hugging Face CLI:
+Here `--output_dir` is required, while `HF_ENDPOINT`, `--subset`, `--remove_parquet_after_extract`, and `--force_reextract` are optional. If `--subset` is omitted, the script processes all RMIS subsets.
 
-```bash
-hf download jiangab/RMIS --repo-type dataset
-```
+In the RMIS project, the Hugging Face route is mainly intended to treat this repository as a **cloud storage backend** and materialize the data into the same local wav-style directory layout used by the other RMIS download paths.
 
-Please note that `load_dataset` is typically used **one configuration at a time**, while whole-repository download is useful when you want all released subsets stored locally.
+## Additional usage
 
-## Recommended usage in the RMIS project
+If you are interested in more customized workflows, you may also directly use the metadata and parquet assets attached to this Hugging Face repository to develop your own data loading, conversion, or preprocessing utilities.
 
-If your goal is to **benchmark a model on RMIS**, this dataset repository is only one part of the workflow. A typical setup is:
+However, for most users who want to reproduce RMIS experiments, the recommended path is still:
 
-1. Load a subset from this Hugging Face dataset repository.
-2. Extract signal representations with your model.
-3. Evaluate the representations with the RMIS benchmark pipeline from the GitHub repository.
+1. Download the RMIS GitHub repository.
+2. Follow the benchmark instructions there.
+3. Use the provided Hugging Face download-and-extract script when you prefer the Hugging Face storage route.
 
-In other words, this repository provides the **benchmark data**, while the RMIS codebase provides the **standardized evaluation**.
+For more details about RMIS, including benchmark construction, evaluation, and usage, please refer to the [RMIS GitHub repository](https://github.com/jianganbai/RMIS).
 
 ## Acknowledgements